Discovering interpretable Lagrangian of dynamical systems from data

DOI10.1016/J.CPC.2023.108960arXiv2302.04400MaRDI QIDQ6086792

Tapas Tripura, Souvik Chakraborty

Publication date: 10 November 2023

Published in: Computer Physics Communications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2302.04400

zbMATH Keywords

conservation lawdifferential equationexplainable artificial intelligenceequation discoveryLagrangian discovery

Mathematics Subject Classification ID

Lagrange's equations (70H03) Approximation methods and numerical treatment of dynamical systems (37M99)

Cites Work

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• Discovering governing equations from data by sparse identification of nonlinear dynamical systems
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• Transfer learning based multi-fidelity physics informed deep neural network
• Deep learning of dynamics and signal-noise decomposition with time-stepping constraints
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Wavelet neural operator for solving parametric partial differential equations in computational mechanics problems

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